Wireless Communication Apparatus, Wireless Communication Method, Wireless Communication System, and Computer Program

ABSTRACT

There is provided a wireless communication apparatus including: a matrix estimating unit that estimates a channel matrix of N rows and M columns (N and M are natural numbers); a selecting unit that selects S rows (S is a natural number, and S&lt;min(M, N)) from the channel matrix estimated by the matrix estimating unit and generates a sub-channel matrix of S rows and M columns; and an arithmetic unit that calculates an antenna weighting coefficient matrix based on the sub-channel matrix generated by the selecting unit. The selecting unit selects the S rows that allow the size of the sub-channel matrix to be the maximum from all matrices having N rows from which the S rows can be selected.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a wireless communication apparatus, awireless communication method, a computer program, and a wirelesscommunication system, and more particularly, to a wireless communicationapparatus, a wireless communication method, a computer program, and awireless communication system performing communication using a MIMO(multiple input multiple output) scheme.

2. Description of the Related Art

A wireless communication system has been proposed in which a transmitterand a receiver each have a plurality of antennas and space divisionmultiplexing communication (MIMO scheme: multiple input multiple output)is performed using the plurality of antennas to increase transmissioncapacity.

FIG. 17 is a conceptual diagram illustrating a wireless communicationsystem using the MIMO scheme. In a wireless communication system 1 shownin FIG. 17, a transmitter 11 includes M antennas 12 a, 12 b, . . . , 12m, and a receiver 21 includes N antennas 22 a, 22 b, . . . , 22 n.

The transmitter 11 performs space/time division multiplexing on Ktransmission data, distributes the data to M antennas 12 a, 12 b, . . ., 12 m, and transmits the data through channels. Then, the receiver 21receives the signals transmitted through the channels using N antennas22 a, 22 b, 22 n, and performs space/time division demultiplexing on thereceived signals to obtain K reception data.

Therefore, the MIMO scheme is a communication system using channelcharacteristics in which the transmitter 11 distributes transmissiondata to a plurality of antennas and transmits it through the antennas,and the receiver 21 receives signals using a plurality of antennas andprocesses the signals to obtain reception data.

There are various data transmission systems using the MIMO scheme. As anideal example of the data transmission system using the MIMO scheme, aneigenmode transmission system has been known which uses the singularvalue decomposition (SVD) or the eigenvalue decomposition of a channelmatrix.

FIG. 18 is a conceptual diagram illustrating the eigenmode transmissionsystem. In the eigenmode transmission system using SVD, singular valuedecomposition is performed on a channel matrix H having channelinformation between transmitting and receiving antennas as elements tocalculate UDV^(H) (V^(H) indicates a complex conjugate transposed matrixof a matrix V). Singular value decomposition is performed on the channelmatrix H to calculate UDV^(H), the matrix V is given as atransmitter-side antenna weighting coefficient matrix, and a matrixU^(H) (U^(H) indicates a complex conjugate transposed matrix of a matrixU) is given as a receiver-side antenna weighting coefficient matrix. Inthis way, a channel can be represented by a diagonal matrix D having thesquare root (singular value) of an eigenvalue λ₁ of a covariance matrix(H^(H)H or HH^(H)) of the channel matrix H. Therefore, it is possible tomultiplex a signal and transmit the multiplexed signal, without anycrosstalk.

When the number of antennas of the transmitter 11 is M, a transmissionsignal x′ is represented by an M×1 vector. When the number of antennasof the receiver 21 is N, a received signal y′ is represented by an N×1vector. In addition, a channel matrix is represented as a matrix H of Nrows and M columns. An element h_(ij) of the channel matrix H is atransfer function from a j-th transmitting antenna to an i-th receivingantenna (1≦i≦N and 1≦j≦M). The received signal vector y′ is obtained byadding a noise vector n to the multiplication of the channel matrix Hand the transmission signal vector x′, as represented by the followingExpression 1.

y′=Hx′+n  (Expression 1)

As described above, when singular value decomposition is performed onthe channel matrix H, the following Expression 2 is obtained.

H=UDV^(H)  (Expression 2)

The transmitter-side antenna weighting coefficient matrix V and thereceiver-side antenna weighting coefficient matrix U^(H) are unitarymatrices that satisfy the following Expressions 3 and 4. In thefollowing Expressions, I indicates a unitary matrix.

U^(H)U=I  (Expression 3)

V^(H)V=I  (Expression 4)

That is, the antenna weighting coefficient matrix U^(H) of the receiver21 is obtained by arranging the normalized eigenvectors of HH^(H), andthe antenna weighting coefficient matrix V of the transmitter 11 isobtained by arranging the normalized eigenvectors of H^(H)H. Inaddition, D indicates a diagonal matrix having the square root of (thesingular value of H) of the eigenvalue of H^(H)H or HH^(H) as a diagonalcomponent. That is, when the smaller one of the number M of transmittingantennas of the transmitter 11 and the number N of receiving antennas ofthe receiver 21 is referred to as L (=min(M, N)), the matrix D is asquare matrix of L rows and L columns. That is, the matrix D can berepresented by the following Expression 5.

$\begin{matrix}{D = \begin{pmatrix}\sqrt{\lambda_{1}} & 0 & \ldots & 0 \\0 & \sqrt{\lambda_{2}} & \ldots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \ldots & \sqrt{\lambda_{L}}\end{pmatrix}} & ( {{Expression}\mspace{14mu} 5} )\end{matrix}$

When the transmitter 11 multiplies data by the antenna weightingcoefficient matrix V and transmits the data and the receiver 21 receivesa signal and multiplies the signal by the complex conjugate transposedmatrix U^(H), the received signal y is represented by the followingExpression 6 since the matrix U of N rows and L columns and the matrix Vof M rows and L columns are unitary matrices.

y=U^(H)y′

=U ^(H)(Hx′+n)

=U ^(H)(HVx+n)

=U ^(H)(UDV ^(H))Vx+U ^(H) n

=(U ^(H) U)D(V ^(H) V)x+U ^(H) n

=IDIx+U ^(H) n

=Dx+U ^(H) n  (Expression 6)

In this case, each of the received signal y and the transmission signalx is a vector of L rows and one column. Since the matrix D is a diagonalmatrix, each transmission signal transmitted from the transmitter 11 canbe received by the receiver 21 without any crosstalk. Since the diagonalelement of the matrix D is the square sum of the eigenvalue λ_(i)(1≦i≦L), the power of each reception signal is λ_(i) times the power ofeach transmission signal. In addition, for the noise component n, sincethe column of U is an eigenvector having a norm that is normalized to 1,U^(H)n does not change the noise power thereof. Therefore, UHn is avector of L rows and one column, and the received signal y and thetransmission signal x have the same size.

As such, in the eigenmode transmission system using the MIMO scheme, itis possible to obtain a plurality of independent logic pulses having thesame frequency at the same time without any crosstalk. That is, it ispossible to wirelessly transmit a plurality of data using the samefrequency at the same time, thereby improving a transmission rate.

SUMMARY OF THE INVENTION

In the MIMO transmission, an antenna weighting method in thetransmitter, particularly, a weighting method for eigenmode transmissioncan be represented by the above-mentioned Expressions. JP-A No.2005-160030 also discloses a weighting method using expressions.

However, in order to perform eigenmode transmission, when the number ofantennas in the transmitter is M and the number of antennas in thereceiver is N, computation needs to be performed on a channel matrix ofN rows and M columns. In this case, in order to obtain idealcharacteristics even when the number S of data streams to be transmittedis smaller than the number of antennas, computation needs to beperformed on the channel matrix of N rows and M columns. In this case,the number S of data streams means the number of pulses that areactually used among the independent pulses for the eigenmodetransmission, and is equal to or smaller than L.

For example, when the number of transmitting antennas is 4, the numberof receiving antennas is 4, and the number of data streams is 2, it isnecessary to perform computation, such as SVD, on a channel matrix H of4 rows and 4 columns in order to perform eigenmode transmission. Whensingular value decomposition is performed on the channel matrix H,H=UDV^(H) is satisfied. In this case, each of the matrices U, D, and Vis a unitary matrix of 4 rows and 4 columns. In the matrix V obtained bySVD, two columns are used as an antenna weighting coefficient matrix totransmit two data streams.

In general, an excessively large amount of computation is needed tocalculate the antenna weighting coefficient matrix requires, as in SVD.As the size of an input channel matrix is increased or as the rank of amatrix is increased, the amount of computation is sharply increased.When the communication system is actually used, the amount ofcalculation depends on the size of a circuit or a computation time.Therefore, the amount of calculation needs to be small. However, asdescribed above, particularly, even when the number of data streams issmaller than the number of antennas, the amount of calculation dependson the number of antennas, which is not preferable in terms of themounting of the communication system.

The present invention has been made in views of the above issues, and itis desirable to provide a wireless communication apparatus, a wirelesscommunication method, a computer program, and a wireless communicationsystem capable of preventing an increase in the amount of computationwhen the maximum value of the number of data streams to be transmittedor received is smaller than the number of antennas used to transmitdata.

According to an embodiment of the present invention, there is provided awireless communication apparatus including: a matrix estimating unitthat estimates a channel matrix of N rows and M columns (N and M arenatural numbers); a selecting unit that selects S rows (S is a naturalnumber, and S<MIN(M, N)) from the channel matrix estimated by the matrixestimating unit and generates a sub-channel matrix of S rows and Mcolumns; and an arithmetic unit that calculates an antenna weightingcoefficient matrix based on the channel matrix estimated by the matrixestimating unit and the sub-channel matrix generated by the selectingunit. The arithmetic unit includes: a weighted channel matrix generatingunit that multiplies the channel matrix by a temporary antenna weightingcoefficient matrix calculated based on the sub-channel matrix togenerate a weighted channel matrix; a reverse weighted channel matrixgenerating unit that transposes the channel matrix and the weightedchannel matrix, and multiplies the transposed channel matrix by areverse antenna weighting coefficient matrix calculated based on thetransposed weighted channel matrix to generate a reverse weightedchannel matrix; and an antenna weighting coefficient matrix calculatingunit that calculates the antenna weighting coefficient matrix based on atransposed reverse weighted channel matrix.

The weighted channel matrix generating unit may repeatedly generate theweighted channel matrix using the antenna weighting coefficient matrixcalculated by the antenna weighting coefficient matrix calculating unitinstead of the temporary antenna weighting coefficient matrix.

The arithmetic unit may include: a first coefficient calculating unitthat calculates the temporary antenna weighting coefficient matrix basedon the sub-channel matrix; a first matrix multiplying unit thatmultiplies the channel matrix by the temporary antenna weightingcoefficient matrix to generate the weighted channel matrix; a firsttransposition unit that transposes the channel matrix and the weightedchannel matrix; a second coefficient calculating unit that calculatesthe reverse antenna weighting coefficient matrix based on the transposedweighted channel matrix; a second matrix multiplying unit thatmultiplies the transposed channel matrix by the reverse antennaweighting coefficient matrix to generate the reverse weighted channelmatrix; a second transposition unit that transposes the reverse weightedchannel matrix; and a third coefficient calculating unit that calculatesthe antenna weighting coefficient matrix based on the transposed reverseweighted channel matrix.

The first coefficient calculating unit may calculate the temporaryantenna weighting coefficient matrix based on the sub-channel matrix andthe channel matrix. The second coefficient calculating unit maycalculate the reverse antenna weighting coefficient matrix based on thetransposed weighted channel matrix and the transposed channel matrix.The third coefficient calculating unit may calculate the antennaweighting coefficient matrix based on the transposed reverse weightedchannel matrix and the channel matrix.

At least one of the first to third coefficient calculating units mayinclude: a fourth coefficient calculating unit that calculates thetemporary antenna weighting coefficient matrix based on the sub-channelmatrix; a third matrix multiplying unit that multiplies the channelmatrix by the temporary antenna weighting coefficient matrix to generatethe weighted channel matrix; a singular value decomposing unit thatperforms singular value decomposition on the weighted channel matrix;and a fourth matrix multiplying unit that multiplies a matrix obtainedby the singular value decomposition by the temporary antenna weightingcoefficient matrix to generate the antenna weighting coefficient matrix.

The selecting unit may select the S rows that allow the sum of thesquares of elements of a matrix to be the maximum. The selecting unitmay select the S rows that allow an eigenvalue of a covariance matrix ofa matrix or the maximum value of a singular value of the matrix to bethe maximum. The selecting unit may select the S rows that allow aneigenvalue of a covariance matrix of a matrix or the minimum value of asingular value of the matrix to be the maximum. The selecting unit mayselect the S rows that allow a determinant of a covariance matrix of amatrix to be the maximum. The selecting unit may select the S rows thatallow a value obtained by dividing a determinant of a covariance matrixof a matrix by the sum of the squares of elements of the matrix to bethe maximum.

According to another embodiment of the present invention, there isprovided a wireless communication method including the steps of:estimating a channel matrix of N rows and M columns (N and M are naturalnumbers); selecting S rows (S is a natural number, and S<MIN(M, N)) fromthe estimated channel matrix and generating a sub-channel matrix of Srows and M columns; and calculating an antenna weighting coefficientmatrix based on the estimated channel matrix and the generatedsub-channel matrix. The calculating step includes the sub-steps of:multiplying the channel matrix by a temporary antenna weightingcoefficient matrix calculated based on the sub-channel matrix togenerate a weighted channel matrix; transposing the channel matrix andthe weighted channel matrix, and multiplying the transposed channelmatrix by a reverse antenna weighting coefficient matrix calculatedbased on the transposed weighted channel matrix to generate a reverseweighted channel matrix; and calculating the antenna weightingcoefficient matrix based on a transposed reverse weighted channelmatrix.

According to another embodiment of the present invention, there isprovided a computer program for allowing a computer to execute the stepsof: estimating a channel matrix of N rows and M columns (N and M arenatural numbers); selecting S rows (S is a natural number, and S<MIN(M,N)) from the estimated channel matrix and generating a sub-channelmatrix of S rows and M columns; and calculating an antenna weightingcoefficient matrix based on the estimated channel matrix and thegenerated sub-channel matrix. The calculating-step includes thesub-steps of: multiplying the channel matrix by a temporary antennaweighting coefficient matrix calculated based on the sub-channel matrixto generate a weighted channel matrix; transposing the channel matrixand the weighted channel matrix, and multiplying the transposed channelmatrix by a reverse antenna weighting coefficient matrix calculatedbased on the transposed weighted channel matrix to generate a reverseweighted channel matrix; and calculating the antenna weightingcoefficient matrix based on a transposed reverse weighted channelmatrix.

According to another embodiment of the present invention, there isprovided a wireless communication system including: a first wirelesscommunication apparatus; and a second wireless communication apparatus.The first wireless communication apparatus includes a transmitting unitthat transmits reference signals to the second wireless communicationapparatus through a plurality of antennas. The second wirelesscommunication apparatus includes: a matrix estimating unit thatestimates a channel matrix of N rows and M columns (N and M are naturalnumbers) based on the reference signals received through a plurality ofantennas; a selecting unit that selects S rows (S is a natural number,and S<MIN(M, N)) from the channel matrix estimated by the matrixestimating unit and generates a sub-channel matrix of S rows and Mcolumns; and an arithmetic unit that calculates an antenna weightingcoefficient matrix based on the channel matrix estimated by the matrixestimating unit and the sub-channel matrix generated by the selectingunit. The arithmetic unit includes: a weighted channel matrix generatingunit that multiplies the channel matrix by a temporary antenna weightingcoefficient matrix calculated based on the sub-channel matrix togenerate a weighted channel matrix; a reverse weighted channel matrixgenerating unit that transposes the channel matrix and the weightedchannel matrix, and multiplies the transposed channel matrix by areverse antenna weighting coefficient matrix calculated based on thetransposed weighted channel matrix to generate a reverse weightedchannel matrix; and an antenna weighting coefficient matrix calculatingunit that calculates the antenna weighting coefficient matrix based on atransposed reverse weighted channel matrix.

As described above, according to the above-mentioned embodiments of thepresent invention, it is possible to provide a wireless communicationapparatus, a wireless communication method, a computer program, and awireless communication system capable of preventing an increase in theamount of computation when the maximum value of the number of datastreams to be transmitted or received is smaller than the number ofantennas used to transmit data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a wireless communication system 10according to a first embodiment of the present invention;

FIG. 2 is a diagram illustrating the structure of a weightingcoefficient matrix calculating circuit 104 according to the firstembodiment of the present invention;

FIG. 3 is a diagram illustrating the structure of an arithmetic circuit114 according to the first embodiment of the present invention;

FIG. 4 is a flowchart illustrating a method of calculating an antennaweighting coefficient matrix W according to the first embodiment of thepresent invention;

FIG. 5A is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention;

FIG. 5B is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention;

FIG. 5C is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention;

FIG. 5D is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention;

FIG. 5E is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention;

FIG. 5F is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention;

FIG. 6A is a bar graph illustrating the convergence of communicationcapacity;

FIG. 6B is a bar graph illustrating the convergence of communicationcapacity;

FIG. 6C is a bar graph illustrating the convergence of communicationcapacity;

FIG. 6D is a bar graph illustrating the convergence of communicationcapacity;

FIG. 6E is a bar graph illustrating the convergence of communicationcapacity;

FIG. 6F is a bar graph illustrating the convergence of communicationcapacity;

FIG. 7 is a diagram illustrating the structure of an arithmetic circuit214 according to a second embodiment of the present invention;

FIG. 8 is a flowchart illustrating a method of calculating an antennaweighting coefficient matrix W according to the second embodiment of thepresent invention;

FIG. 9 is a bar graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the second embodiment of the presentinvention;

FIG. 10A is a bar graph illustrating the ratio of communicationcapacities;

FIG. 10B is a bar graph illustrating the ratio of communicationcapacities;

FIG. 10C is a bar graph illustrating the ratio of communicationcapacities;

FIG. 10D is a bar graph illustrating the ratio of communicationcapacities;

FIG. 10E is a bar graph illustrating the ratio of communicationcapacities;

FIG. 10F is a bar graph illustrating the ratio of communicationcapacities;

FIG. 11 is a diagram illustrating the structure of an arithmetic circuit414 according to a third embodiment of the present invention;

FIG. 12 is a diagram illustrating the structure of a first coefficientcalculating circuit 422 a according to the third embodiment of thepresent invention;

FIG. 13 is a flowchart illustrating a method of calculating an antennaweighting coefficient matrix W according to the third embodiment of thepresent invention;

FIG. 14A is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the third embodiment of the presentinvention;

FIG. 14B is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the third embodiment of the presentinvention;

FIG. 14C is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the third embodiment of the presentinvention;

FIG. 14D is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the third embodiment of the presentinvention;

FIG. 14E is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the third embodiment of the presentinvention;

FIG. 14F is a line graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the third embodiment of the presentinvention;

FIG. 15 is a diagram illustrating the structure of an arithmetic circuit414′ according to a modification of the third embodiment of the presentinvention;

FIG. 16A is a bar graph illustrating the convergence of communicationcapacity;

FIG. 16B is a bar graph illustrating the convergence of communicationcapacity;

FIG. 16C is a bar graph illustrating the convergence of communicationcapacity;

FIG. 16D is a bar graph illustrating the convergence of communicationcapacity;

FIG. 16E is a bar graph illustrating the convergence of communicationcapacity;

FIG. 16F is a bar graph illustrating the convergence of communicationcapacity;

FIG. 17 is a conceptual diagram illustrating a wireless communicationsystem using a MIMO scheme; and

FIG. 18 is a conceptual diagram illustrating eigenmode transmission.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, preferred embodiments of the present invention will bedescribed in detail with reference to the appended drawings. Note that,in this specification and the appended drawings, structural elementsthat have substantially the same function and structure are denoted withthe same reference numerals, and repeated explanation of thesestructural elements is omitted.

First Embodiment

First, a wireless communication apparatus and a wireless communicationsystem using the wireless communication apparatus according to a firstembodiment of the present invention will be described.

FIG. 1 is a diagram illustrating a wireless communication system 10according to the first embodiment of the present invention. Hereinafter,the wireless communication system 10 according to the first embodimentof the present invention will be described with reference to FIG. 1.

As shown in FIG. 1, the wireless communication system 10 according tothe first embodiment of the present invention includes a transmitter 100and a receiver 200.

Each of the transmitter 100 and the receiver 200 is an example of thewireless communication apparatus according to the present invention. Thetransmitter 100 transmits data to the receiver 200 using a plurality ofantennas by a MIMO scheme, and the receiver 200 receives datatransmitted from the transmitter 100 using a plurality of antennas bythe MIMO scheme.

A data link from the transmitter 100 to the receiver 200 is defined as aforward link, and a link from the receiver 200 to the transmitter 100 isdefined as a backward link. A channel matrix H of the forward link is amatrix of N rows and M columns, and a channel matrix H_(B) of thebackward link is a matrix of M rows and N columns. In addition, ideally,the channel matrix satisfies the following relationship represented byExpression 7.

H_(B)=H^(T)  (Expression 7)

The transmitter 100 includes M antennas 101 a, 101 b, . . . , 101 m, achannel estimating circuit 102, a weighting coefficient matrixcalculating circuit 104, and a transmitting circuit 106. Thetransmitting circuit 106 includes a weighting circuit 110.

The channel estimating circuit 102 estimates a channel matrix H_(B) fromthe receiver 200 to the transmitter 100 using a known pattern (forexample, a reference signal) transmitted from the receiver 200. A knownmethod can be used to estimate the channel matrix, and thus a detaileddescription thereof will be omitted. Ideally, H_(B)=H^(T) is satisfiedby Expression 7. Therefore, it is possible to estimate a channel matrixH using the channel estimating circuit 102. The channel matrix Hestimated by the channel estimating circuit 102 is input to theweighting coefficient matrix calculating circuit 104.

The weighting coefficient matrix calculating circuit 104 uses thechannel matrix H transmitted from the channel estimating circuit 102 tocalculate an antenna weighting coefficient matrix W for a transmissionbeam-forming. The antenna weighting coefficient matrix W generated bythe weighting coefficient matrix calculating circuit 104 is input to theweighting circuit 110.

The transmitting circuit 106 receives a transmission data streamtransmitted from the transmitter 100 to the receiver 200, and generatestransmission signals to be transmitted from the antennas 101 a, 101 b, .. . , 101 m. The weighting circuit 110 multiplies a transmission datastream x transmitted from the transmitter 100 to the receiver 200 by theantenna weighting coefficient matrix W generated by the weightingcoefficient matrix calculating circuit 104 to generate a transmissionsignal x′. The transmission signal x′ generated by the weighting circuit110 is radiated from each of the antennas, thereby performingtransmission beam-forming.

The channel estimating circuit 102 may be provided in the receiver 200.When the channel estimating circuit 102 is provided in the receiver 200,it receives a known pattern transmitted from the transmitter 100, andestimates the channel matrix H based on the received pattern. Thereceiver 200 notifies the channel matrix H to the transmitter 100 usingany method (for example, a method of transmitting data related to thechannel matrix H from the receiver 200 to the transmitter 100).

The weighting coefficient matrix calculating circuit 104 may also beprovided in the receiver 200. When the weighting coefficient matrixcalculating circuit 104 is provided in the receiver 200, it calculatesthe antenna weighting coefficient matrix W based on the channel matrixH. Then, the receiver 200 notifies the antenna weighting coefficientmatrix W to the transmitter 100 using any method (for example, a methodof transmitting data related to the antenna weighting coefficient matrixW from the receiver 200 to the transmitter 100).

The transmitting circuit 202 generates transmission signals to betransmitted from the receiver 200 to the transmitter through theantennas 201 a, 201 b, . . . , 201 n. The transmitting circuit 202transmits a known pattern (for example, a reference signal) used for thechannel estimating circuit 102 to estimate the channel matrix H to thetransmitter 100 through the antennas 201 a, 201 b, . . . , 201 n.

The wireless communication system 10 according to the first embodimentof the present invention has been described above with reference toFIG. 1. Next, the structure of the weighting coefficient matrixcalculating circuit 104 according to the first embodiment of the presentinvention will be described.

FIG. 2 is a diagram illustrating the structure of the weightingcoefficient matrix calculating circuit 104 according to the firstembodiment of the present invention. Hereinafter, the structure of theweighting coefficient matrix calculating circuit according to the firstembodiment of the present invention will be described with reference toFIG. 2.

As shown in FIG. 2, the weighting coefficient matrix calculating circuit104 according to the first embodiment of the present invention includesa selecting circuit 112 and an arithmetic circuit 114.

The selecting circuit 112 appropriately selects S rows from an inputchannel matrix H of N rows and M columns, and outputs a sub-channelmatrix H_(S). Here, S indicates the maximum value of the number of datastreams, and S, N, and M satisfy S<min(M, N). The selecting circuit 112outputs the sub-channel matrix H_(S) of S rows and M columns. Thesub-channel matrix H_(S) output from the selecting circuit 112 istransmitted to the arithmetic circuit 114.

The arithmetic circuit 114 calculates the antenna weighting coefficientmatrix W for a transmission beam-forming based on the channel matrix Hestimated by the channel estimating circuit 102 and the sub-channelmatrix H_(S) transmitted from the selecting circuit 112 and outputs thecalculated antenna weighting coefficient matrix W. The antenna weightingcoefficient matrix W output from the arithmetic circuit 114 istransmitted to the weighting circuit 110. A method of calculating theantenna weighting coefficient matrix W based on the sub-channel matrixH_(S) in the arithmetic circuit 114 will be described in detail below.

The structure of the weighting coefficient matrix calculating circuit104 according to the first embodiment of the present invention has beendescribed above with reference to FIG. 2. Next, the structure of thearithmetic circuit 114 according to the first embodiment of the presentinvention will be described.

FIG. 3 is a diagram illustrating the structure of the arithmetic circuit114 according to the first embodiment of the present invention.Hereinafter, the structure of the arithmetic circuit 114 according tothe first embodiment of the present invention will be described withreference to FIG. 3.

As shown in FIG. 3, the arithmetic circuit 114 according to the firstembodiment of the present invention includes coefficient calculatingcircuits 322 a, 322 b, and 322 c, matrix multiplying circuits 324 a and324 b, and transposition circuits 326 a, 326 b, and 326 c.

The coefficient calculating circuit 322 a calculates a temporary antennaweighting coefficient matrix W_(T) based on the sub-channel matrixH_(S). For example, the coefficient calculating circuit 322 a can usesingular value decomposition to calculate the temporary antennaweighting coefficient matrix W_(T). When singular value decomposition isperformed on the sub-channel matrix H_(S), the above-mentionedExpression 8 is obtained.

H_(S)=UDV^(H)  (Expression 8)

When the temporary antenna weighting coefficient matrix W_(T) iscalculated by singular value decomposition, the coefficient calculatingcircuit 322 a uses the matrix V (right singular matrix) of theExpression 8 as the temporary antenna weighting coefficient matrixW_(T). The coefficient calculating circuit 322 a outputs the calculatedtemporary antenna weighting coefficient matrix W_(T) to the matrixmultiplying circuit 324 a.

The matrix multiplying circuit 324 a is for multiplying matrices. Thematrix multiplying circuit 324 a according to this embodiment multipliesthe channel matrix H estimated by the channel estimating circuit 102 bythe temporary antenna weighting coefficient matrix W_(T) calculated bythe coefficient calculating circuit 322 a to generate a weighted channelmatrix H_(W)(H_(W)=HW_(T)). In this case, H_(W) indicates a matrix of Mrows and S columns. The weighted channel matrix H_(W) generated by thematrix multiplying circuit 324 a is output to the transposition circuit326 a.

The transposition circuit 326 a is for transposing a matrix. Thetransposition circuit 326 a according to this embodiment receives theweighted channel matrix H_(W) generated by the matrix multiplyingcircuit 324 a, transposes the weighted channel matrix H_(W), and outputsa transposed weighted channel matrix H_(W) ^(T). The transposed weightedchannel matrix H_(W) ^(T) is input to the coefficient calculatingcircuit 322 b.

The coefficient calculating circuit 322 b calculates a reverse antennaweighting coefficient matrix W_(R) based on the transposed weightedchannel matrix H_(W) ^(T) output from the transposition circuit 326 a.For example, the coefficient calculating circuit 322 b can use singularvalue decomposition to calculate the reverse antenna weightingcoefficient matrix W_(R). When singular value decomposition is performedon the transposed weighted channel matrix H_(W) ^(T), the followingExpression 9 is obtained.

H_(W) ^(T)=UDV^(H)  (Expression 9)

When the reverse antenna weighting coefficient matrix W_(R) iscalculated by singular value decomposition, the coefficient calculatingcircuit 322 b uses the matrix (right singular matrix) V of Expression 13as the reverse antenna weighting coefficient matrix W_(R). Thecoefficient calculating circuit 322 b outputs the calculated reverseantenna weighting coefficient matrix W_(R) to the matrix multiplyingcircuit 324 b.

The transposition circuit 326 b is for transposing a matrix. Thetransposition circuit 326 b according to this embodiment receives thechannel matrix H estimated by the channel estimating circuit 102,transposes the channel matrix H, and outputs a transposed channel matrixH^(T). The transposed channel matrix H^(T) is input to the matrixmultiplying circuit 324 b.

The matrix multiplying circuit 324 b is for multiplying matrices. Thematrix multiplying circuit 324 b according to this embodiment multipliesthe reverse antenna weighting coefficient matrix W_(R) output from thecoefficient calculating circuit 322 b by the transposed channel matrixH^(T) output from the transposition circuit 326 b to generate a reverseweighted channel matrix H_(WR) (H_(WR)=H^(T)W_(R)). In the case, thereverse weighted channel matrix H_(WR) includes N rows and S columns.The reverse weighted channel matrix H_(WR) generated by the matrixmultiplying circuit 324 b is input to the transposition circuit 326 c.

The transposition circuit 326 c is for transposing a matrix. Thetransposition circuit 326 c according to this embodiment transposes thereverse weighted channel matrix H_(WR) generated by the matrixmultiplying circuit 324 b to generate a transposed reverse weightedchannel matrix H_(WR) ^(T). The transposed reverse weighted channelmatrix H_(WR) ^(T) is input to the coefficient calculating circuit 322c.

The coefficient calculating circuit 322 c receives the transposedreverse weighted channel matrix H_(WR) ^(T) output from thetransposition circuit 326 c, and generates the antenna weightingcoefficient matrix W based on the transposed reverse weighted channelmatrix H_(WR) ^(T). For example, the coefficient calculating circuit 322c can use singular value decomposition to calculate the antennaweighting coefficient matrix W. When singular value decomposition isperformed on the transposed reverse weighted channel matrix H_(WR) ^(T),the following Expression 14 is obtained.

H_(WR) ^(T)=UDV^(H)  (Expression 10)

When the antenna weighting coefficient matrix W is calculated bysingular value decomposition, the coefficient calculating circuit 322 cuses the matrix (right singular matrix) V of Expression 10 as theantenna weighting coefficient matrix W. The coefficient calculatingcircuit 322 c outputs the calculated antenna weighting coefficientmatrix W to the weighting circuit 110.

The antenna weighting coefficient matrix W output from the coefficientcalculating circuit 322 c may be input to the matrix multiplying circuit324 a again. It is possible to improve communication characteristics byrepeatedly performing a computing process using the antenna weightingcoefficient matrix W output from the coefficient calculating circuit 322c.

The structure of the arithmetic circuit 114 according to the firstembodiment of the present invention has been described above withreference to FIG. 3. Next, a method of calculating the antenna weightingcoefficient matrix W according to the first embodiment of the presentinvention will be described.

FIG. 4 is a flowchart illustrating the method of calculating the antennaweighting coefficient matrix W according to the first embodiment of thepresent invention. Hereinafter, the method of calculating the antennaweighting coefficient matrix W according to the first embodiment of thepresent invention will be described with reference to FIG. 4.

When the channel matrix H of N rows and M columns is input to theweighting coefficient matrix calculating circuit 104, the selectingcircuit 112 appropriately selects S rows from the channel matrix H andgenerates the sub-channel matrix H_(S) (Step S302). When the selectingcircuit 112 generates the sub-channel matrix H_(S), the coefficientcalculating circuit 322 a calculates the temporary antenna weightingcoefficient matrix W_(T) based on the generated sub-channel matrix H_(S)(Step S304). As described above, for example, the coefficientcalculating circuit 322 a can use singular value decomposition indicatedin Expression 8 to calculate the temporary antenna weighting coefficientmatrix W_(T).

When the temporary antenna weighting coefficient matrix W_(T) iscompletely generated in Step S304, the matrix multiplying circuit 324 amultiplies the channel matrix H by the temporary antenna weightingcoefficient matrix W_(T) to generate the weighted channel matrix H_(W)(Step S306).

When the weighted channel matrix H_(W) is generated in Step S306, thetransposition circuit 326 a transposes the weighted channel matrixH_(W), and the coefficient calculating circuit 322 b performs singularvalue decomposition on the transposed weighted channel matrix H_(W) ^(T)to generate the reverse antenna weighting coefficient matrix W_(R) (StepS308).

When the reverse antenna weighting coefficient matrix W_(R) is generatedin Step S308, the matrix multiplying circuit 324 b multiplies thetransposed channel matrix H^(T) transposed from the channel matrix H bythe transposition circuit 326 b by the reverse antenna weightingcoefficient matrix W_(R). The reverse weighted channel matrix H_(WR) isobtained by the multiplication of the transposed channel matrix H^(T)and the reverse antenna weighting coefficient matrix W_(R) (Step S310).

When the reverse weighted channel matrix H_(WR) is obtained in StepS310, the transposition circuit 326 c transposes the reverse weightedchannel matrix H_(WR) to obtain the transposed reverse weighted channelmatrix H_(WR) ^(T). Then, the coefficient calculating circuit 322 cperforms singular value decomposition on the transposed reverse weightedchannel matrix H_(WR) ^(T) to calculate the antenna weightingcoefficient matrix W (Step 312).

In this way, finally, the matrix is transposed to obtain the antennaweighting coefficient matrix W. Therefore, it is possible to obtain thesame effects as those when the arithmetic circuit 114 performs a two-waybeam-forming process.

From the relationship of S<min(M, N), since the rank of the matrixsubjected to singular value decomposition is less than that in eigenmodetransmission, the amount of calculation is reduced by a valuecorresponding to the reduction in the rank of the matrix. In addition,the amount of calculation is reduced by a value corresponding to areduction in the number of elements of the matrix generated by thesingular value decomposition. In addition, in order to improvecommunication characteristics, Step S306 to Step S312 may be repeatedlyperformed using the antenna weighting coefficient matrix W output fromthe coefficient calculating circuit 322 c. As described above, the totalnumber of processes is increased, but the amount of calculation of thecomplicated singular value decomposition process is reduced. Therefore,it is possible to prevent an increase in the total amount ofcomputation.

The coefficient calculating circuits 322 a, 322 b, and 322 c may beintegrated with each other, the matrix multiplying circuits 324 a, 324b, and 324 c may be integrated with each other, and the transpositioncircuits 326 a, 326 b, and 326 c may be integrated with each otheraccording to conditions.

FIGS. 5A to 5F are line graphs illustrating the comparison betweeneigenmode transmission and transmission using the antenna weightingcoefficient matrix W calculated by the method of calculating the antennaweighting coefficient matrix W according to the first embodiment of thepresent invention. FIGS. 5A to 5F plot the maximum value and the minimumvalue of the ratios of communication capacity C₁ when S data streams aretransmitted by the antenna weighting coefficient matrix W tocommunication capacity C₀ when S data streams are transmitted byeigenmode transmission (C₁/C₀) for 10000 samples. In the line graphs,the horizontal axis indicates the number of times Steps S306 to S312 arerepeated. In addition, the number of repetitions is zero when thetemporary antenna weighting matrix W_(T) calculated by the coefficientcalculating circuit 322 a is used instead of the antenna weightingcoefficient matrix W.

The communication capacity C is calculated by Expression 11 given below.

C=log₂(det(I+H _(W) ^(H) H _(W) ·P/(Mσ ²)))  (Expression 11)

P indicates transmission power, and is 1 in Expression 11. σ² indicatesnoise power and is 0.1 (SNR=10 dB) in Expression 11. In addition, Mindicates the number of transmitting antennas, and I indicates a unitarymatrix of S rows and S columns. H_(W) indicates a weighted channelmatrix, and is represented by the following. Expression 12. In addition,it is assumed that each element of the channel matrix H has a complexnormal distribution with a mean of 0 and a variance of 1.

H_(W)=HW  (Expression 12)

In FIGS. 5A to 5F, when the number of transmitting antennas is M, thenumber of receiving antennas is N, and the number of rows selected fromthe channel matrix is S, the ratio of communication capacities isrepresented by six combinations of M, N, and S.

FIG. 5A is a line graph illustrating characteristics when (M×N×S) is(3×3×2), FIG. 5B is a line graph illustrating characteristics when(M×N×S) is (4×4×2), and FIG. 5C is a line graph illustratingcharacteristics when (M×N×S) is (4×4×3). In addition, FIG. 5D is a linegraph illustrating characteristics when (M×N×S) is (5×5×2), FIG. 5E is aline graph illustrating characteristics when (M×N×S) is (5×5×3), andFIG. 5F is a line graph illustrating characteristics when (M×N×S) is(5×5×4).

As can be seen from six line graphs shown in FIGS. 5A to 5F, the ratioof the communication capacities converges on 1. That is, thecommunication capacity when the antenna weighting coefficient matrix Wis used converges on the communication capacity during the eigenmodetransmission.

Then, in order to improve the communication capacity, the selectingcircuit 112 uses the following patterns. In the channel matrix H of Nrows and M columns, the number of matrices capable of selecting S rowsfrom N rows is _(N)C_(S). A set of _(N)C_(S) matrices is referred to asG(H).

When one matrix H₀ (H₀ is a matrix of S rows and M columns) is selectedfrom G(H), for example, a large matrix may be selected, and a matrixthat is easy to calculate its inverse matrix (a matrix that is ease tocalculate its inverse matrix is defined as a matrix having highreversibility) may be selected. In this embodiment, when one matrix H₀is selected from G(H), the following five patterns are used. However,the present invention is not limited to the example in which S rows areselected from N rows.

(Pattern 1)

A matrix whose covariance matrix has the largest minimum value of itseigenvalue (or its singular value) is selected from G(H). That is, whenH₀εG(H) and the minimum value of the eigenvalue of H₀ ^(H)H₀ or H₀H₀^(H) (or the singular value of H₀) is λ_(min), a matrix H₀ having thelargest value of λ_(min) is selected, and is output as the sub-channelmatrix H_(S).

(Pattern 2)

A matrix whose covariance matrix has the largest determinant is selectedfrom G(H). That is, a matrix H₀ that satisfies H₀εG(H) and has themaximum value of det(H₀ ^(H)H₀) is selected and is output as thesub-channel matrix H_(S). H₀H indicates a complex conjugate transposedmatrix of the matrix H₀.

(Pattern 3)

A matrix whose covariance matrix has the largest maximum value of itseigenvalue (or its singular value) is selected from G(H). That is, whenH₀εG(H) and the maximum value of the eigenvalue of H₀ ^(H)H₀ or H₀H₀^(H) (or the singular value of H₀) is λ_(max), a matrix H₀ having themaximum value of λ_(max) is selected, and is output as the sub-channelmatrix H_(S).

(Pattern 4)

A matrix in which the sum of the squares of its elements is the maximumis selected from G(H). That is, a matrix H₀ that satisfies H₀εG(H) andhas the maximum value of trace(H₀ ^(H)H₀) is selected and is output asthe sub-channel matrix H_(S).

(Pattern 5)

A matrix that has the maximum value when the determinant of itscovariance matrix is divided by the sum of the squares of its elementsis selected from G(H). That is, a matrix H₀ that satisfies H₀εG(H) andhas the maximum value of det(H₀ ^(H)H₀)/trace(H₀ ^(H)H₀) is selected andis output as the sub-channel matrix H_(S). In the pattern 5, a matrix H₀having the maximum value of det(H₀ ^(H)H₀)^(α)/trace(H₀ ^(H)H₀)^(β) (αand β are arbitrary values) may be selected.

A pattern that selects a large matrix corresponds to the patterns 3 and4, and a pattern that selects a matrix having the highest reversibilitycorresponds to the patterns 1, 2, and 5.

FIGS. 6A to 6F are line graphs illustrating the convergence ofcommunication capacity when the antenna weighting coefficient matrix Wis calculated using the sub-channel matrix H_(S) selected by thepatterns 1 to 5. FIGS. 6A to 6F show the antenna weighting coefficientmatrix W calculated using the sub-channel matrix H_(S) that is selectedby the patterns 1 to 5 when (M×N×S) is (3×3×2).

FIG. 6A is the same as FIG. 5A, FIG. 6B is a line graph illustratingcharacteristics when the sub-channel matrix H_(S) selected by thepattern 1 is used, and FIG. 6C is a line graph illustratingcharacteristics when the sub-channel matrix H_(S) selected by thepattern 2 is used. FIG. 6D is a line graph illustrating characteristicswhen the sub-channel matrix H_(S) selected by the pattern 3 is used,FIG. 6E is a line graph illustrating characteristics when thesub-channel matrix H_(S) selected by the pattern 4 is used, and FIG. 6Fis a line graph illustrating characteristics when the sub-channel matrixH_(S) selected by the pattern 5 is used.

As can be seen from the graphs shown in FIGS. 6A to 6F, in all thepatterns 1 to 5, convergence characteristics are improved, as comparedto when S rows are appropriately selected from N rows of the channelmatrix H of N rows and M columns to generate the sub-channel matrixH_(S). The improvement in the convergence characteristics makes itpossible to shorten a calculation time.

In this embodiment, (M×N×S) is (3×3×2), but other combinations of M, N,and S may be used. In this case, the convergence characteristics arealso improved, as compared to when S rows are appropriately selectedfrom N rows of the channel matrix H of N rows and M columns to generatethe sub-channel matrix H_(S).

The method of calculating the antenna weighting coefficient matrix Waccording to the first embodiment of the present invention has beendescribed above with reference to FIG. 4. As described above, accordingto the first embodiment of the present invention, when the maximum valueof the number of data streams to be subjected to a transmissionbeam-forming process is smaller than the number of antennas, the antennaweighting coefficient matrix W is calculated based on the channel matrixH and the sub-channel matrix H_(S) obtained by extracting the number ofrows corresponding to the maximum value of the number of data streamsfrom the channel matrix H.

As such, since the antenna weighting coefficient matrix W is calculatedbased on the channel matrix H and the sub-channel matrix H_(S), the rankof the matrix used for calculation is reduced, and it is possible toreduce the amount of calculation of SVD, which is a representativeexample of the matrix computation, while maintaining characteristics ata certain level. In addition, it is possible to prevent deterioration ofcharacteristics and reduce the overall size of a weighting coefficientmatrix calculating circuit. When this wireless communication system isactually mounted to a communication apparatus, the amount of computationdepends on the size of a circuit and a computation time. Therefore, itis possible to reduce the size of a circuit and shorten the computationtime.

When the sub-channel matrix H_(S) is generated, it is possible toimprove characteristics while minimizing an increase in the amount ofcomputation by giving row selection conditions.

Second Embodiment

In the first embodiment of the present invention, after the temporaryantenna weighting coefficient matrix W_(T) is calculated from thesub-channel matrix H_(S), the reverse antenna weighting coefficientmatrix W_(R) is obtained by the transposition of the matrix, and theantenna weighting coefficient matrix W is obtained by the transpositionof the matrix. In addition, it is possible to improve the convergencecharacteristics of communication capacity by changing the structures ofthe coefficient calculating circuits 322 a, 322 b, and 322 c shown inFIG. 3. In a second embodiment of the present invention, the antennaweighting coefficient matrix W is calculated using the channel matrix Hand the sub-channel matrix H_(S), in order to achieve the improvement ofthe convergence characteristics, which will be described below.

FIG. 7 is a diagram illustrating the structure of the arithmetic circuit214 according to the second embodiment of the present invention.Hereinafter, the structure of the arithmetic circuit 214 according tothe second embodiment of the present invention will be described withreference to FIG. 7.

The arithmetic circuit 214 shown in FIG. 7 replaces the arithmeticcircuit 114 according to the first embodiment of the present inventionshown in FIG. 2. That is, the arithmetic circuit 214 receives thesub-channel matrix H_(S) selected by the selecting circuit 112 and thechannel matrix H estimated by the channel estimating circuit 102, andoutputs the antenna weighting coefficient matrix W for a transmissionbeam-forming. As shown in FIG. 7, the arithmetic circuit 214 accordingto the second embodiment of the present invention includes a coefficientcalculating circuit 222, matrix multiplying circuits 224 a and 224 b,and an SVD circuit 226.

The coefficient calculating circuit 222 calculates a temporary antennaweighting coefficient matrix W_(T) based on the sub-channel matrixH_(S). For example, singular value decomposition may be used as a methodof calculating the temporary antenna weighting coefficient matrix W_(T)in the coefficient calculating circuit 222. When singular valuedecomposition is performed on the sub-channel matrix H_(S), thefollowing Expression 13 is obtained.

H_(S)=UDV^(H)  (Expression 13)

When the singular value decomposition is performed to calculate thetemporary antenna weighting coefficient matrix W_(T), the coefficientcalculating circuit 222 uses the matrix (right singular matrix) V ofExpression 13 as the temporary antenna weighting coefficient matrixW_(T). The coefficient calculating circuit 222 outputs the calculatedtemporary antenna weighting coefficient matrix W_(T) to the matrixmultiplying circuits 224 a and 224 b.

The matrix multiplying circuit 224 a is for multiplying matrices. Thematrix multiplying circuit 224 a according to this embodiment multipliesthe channel matrix H estimated by the channel estimating circuit 102 bythe temporary antenna weighting coefficient matrix W_(T) calculated bythe coefficient calculating circuit 222 to generate a weighted channelmatrix H_(W) (H_(W)=H^(T)). The matrix H_(W) includes M rows and Scolumns. The weighted channel matrix H_(W) generated by the matrixmultiplying circuit 224 a is output to the SVD circuit 226.

The SVD circuit 226 is for performing singular value decomposition. Inthis embodiment, the SVD circuit 226 receives the weighted channelmatrix H_(W) generated by the matrix multiplying circuit 224 a andperforms the singular value decomposition on the weighted channel matrixH_(W). When the singular value decomposition is performed on theweighted channel matrix H_(W), the following Expression 14 is obtained.

H_(W)=UDV^(H)  (Expression 14)

The matrix (right singular matrix) V obtained by the Expression 14 isoutput to the matrix multiplying circuit 224 b.

The matrix multiplying circuit 224 b is for multiplying matrices. Thematrix multiplying circuit 224 b according to this embodiment multipliesthe temporary antenna weighting coefficient matrix W_(T) calculated bythe coefficient calculating circuit 222 by the matrix V calculated bythe SVD circuit 226 to generate the antenna weighting coefficient matrixW (W=W_(T)V).

The structure of the arithmetic circuit 214 according to the secondembodiment of the present invention has been described with reference toFIG. 7. Next, a method of calculating the antenna weighting coefficientmatrix W according to the second embodiment of the present inventionwill be described.

FIG. 8 is a flowchart illustrating the method of calculating the antennaweighting coefficient matrix W according to the second embodiment of thepresent invention. Hereinafter, the method of calculating the antennaweighting coefficient matrix W according to the second embodiment of thepresent invention will be described with reference to FIG. 8.

When a channel matrix H of N rows and M columns is input to theweighting coefficient matrix calculating circuit 104, the selectingcircuit 112 appropriately selects S rows from the channel matrix H andgenerates a sub-channel matrix H_(s) (Step S202). When the selectingcircuit 112 generates the sub-channel matrix H_(S), the coefficientcalculating circuit 222 calculates the temporary antenna weightingcoefficient matrix W_(T) based on the generated sub-channel matrix H_(S)(Step S204). As described above, for example, the coefficientcalculating circuit 222 can use singular value decomposition asindicated in Expression 13 to calculate the temporary antenna weightingcoefficient matrix W_(T).

When the temporary antenna weighting coefficient matrix W_(T) iscompletely generated in Step S204, the matrix multiplying circuit 224 amultiplies the channel matrix H by the temporary antenna weightingcoefficient matrix W_(T) to generate the weighted channel matrix H_(W)(Step S206). As such, the weighted channel matrix H_(W) is generated,and an antenna weighting coefficient matrix most suitable for thechannel matrix H is calculated again.

When the weighted channel matrix H_(W) is generated in Step S206, theSVD circuit 226 performs singular value decomposition on the weightedchannel matrix H_(W) to obtain the matrix V (Step S208).

When the singular value decomposition is performed on the weightedchannel matrix H_(W) in Step S208, the matrix multiplying circuit 224 bmultiplies the temporary antenna weighting coefficient matrix W_(T)generated in Step S204 by the matrix V obtained in Step S208 tocalculate the antenna weighting coefficient matrix W (Step S210). Therows that are not used in the calculation of the temporary antennaweighting coefficient matrix W_(T) (that is, the rows of the channelmatrix H that are not included in the sub-channel matrix H_(S)) are usedto calculate the antenna weighting coefficient matrix W. Therefore, itis possible to improve characteristics.

From the relationship of S<min(M, N), since the rank of the matrixsubjected to singular value decomposition is less than that in eigenmodetransmission, the amount of calculation is reduced by a valuecorresponding to the reduction in the rank of the matrix. In addition,the amount of calculation is reduced by a value corresponding to areduction in the number of elements of the matrix generated by thesingular value decomposition. As described above, the total number ofprocesses is increased, but the amount of calculation of the complicatedsingular value decomposition process is reduced. Therefore, it ispossible to prevent an increase in the total amount of computation.

In addition, the coefficient calculating circuit 222 and the SVD circuit226 may be integrated with each other according to conditions.

FIG. 9 is a bar graph illustrating the comparison between eigenmodetransmission and transmission using the antenna weighting coefficientmatrix W calculated by the method of calculating the antenna weightingcoefficient matrix W according to the second embodiment of the presentinvention. FIG. 9 plots the average value of the ratios of communicationcapacity C₁ when S data streams are transmitted by using the antennaweighting coefficient matrix W to communication capacity C₀ when S datastreams are transmitted by eigenmode transmission (C₁/C₀) for 10000samples. In FIG. 9, when the number of transmitting antennas is M, thenumber of receiving antennas is N, and the number of rows selected fromthe channel matrix is S, the ratio of communication capacities isrepresented by six combinations of M, N, and S. For example, (3×3×2)indicates that the number of transmitting antennas is 3, the number ofreceiving antennas is 3, and the number of rows selected from thechannel matrix is 2.

The communication capacity C is calculated by the above-mentionedExpression 11. In addition, it is assumed that each element of thechannel matrix H has a complex normal distribution with a mean of 0 anda variance of 1.

As can be seen from FIG. 9, the communication capacity in each case is90% to 95% of the communication capacity in the eigenmode transmission.

Next, the operation of the selecting circuit 212 selecting S rows fromthe channel matrix H by the patterns 1 to 5 described in the firstembodiment of the present invention in order to improve thecommunication capacity will be described.

FIGS. 10A to 10F are bar graphs illustrating the ratio of communicationcapacities when the sub-channel matrices H_(S) selected by the patterns1 to 5 are used to calculate the antenna weighting coefficient matrix W.Similar to FIG. 9, FIGS. 10A to 10F plot the average value of the ratiosof the communication capacity C₁ when S data streams are transmitted byusing the antenna weighting coefficient matrix W to the communicationcapacity C₀ when S data streams are transmitted by eigenmodetransmission (C₁/C₀) for 10000 samples.

In the bar graphs shown in FIGS. 10A to 10F, “sel0” in the horizontalaxis indicates characteristics when S rows are appropriately selectedfrom N rows of the channel matrix H of N rows and M columns to generatethe sub-channel matrix H_(S), which are the same as those in FIG. 4. Inaddition, “sel0” to “sel5” in the horizontal axis indicatecharacteristics when the sub-channel matrices H_(S) selected by thepatterns 1 to 5 are generated, respectively.

FIG. 10A is a bar graph illustrating characteristics when (M×N×S) is(3×3×2), FIG. 10B is a bar graph illustrating characteristics when(M×N×S) is (4×4×2), and FIG. 10C is a bar graph illustratingcharacteristics when (M×N×S) is (4×4×3). In addition, FIG. 10D is a bargraph illustrating characteristics when (M×N×S) is (5×5×2), FIG. 10E isa bar graph illustrating characteristics when (M×N×S) is (5×5×3), andFIG. 10F is a bar graph illustrating characteristics when (M×N×S) is(5×5×4).

As can be seen from six bar graphs shown in FIGS. 10A to 10F, in all thepatterns 1 to 5, communication characteristics are improved as comparedto the case in which S rows are appropriately selected from N rows ofthe channel matrix H of N rows and M columns to generate the sub-channelmatrix H_(S).

The method of calculating the antenna weighting coefficient matrix Waccording to the second embodiment of the present invention has beendescribed with reference to FIG. 8. As described above, according to thesecond embodiment of the present invention, when the maximum value ofthe number of data streams to be subjected to a transmissionbeam-forming process is smaller than the number of antennas, the antennaweighting coefficient matrix W is calculated based on the channel matrixH and the sub-channel matrix H_(S) obtained by extracting the number ofrows corresponding to the maximum value of the number of data streamsfrom the channel matrix H.

As such, since the antenna weighting coefficient matrix W is calculatedbased on the channel matrix H and the sub-channel matrix H_(S), it ispossible to prevent an increase in the amount of calculation whilemaintaining characteristics at a certain level. When this wirelesscommunication system is actually mounted to a communication apparatus,the amount of computation depends on the size of a circuit and acomputation time. Therefore, it is possible to reduce the size of acircuit and shorten the computation time.

When the sub-channel matrix H_(S) is generated, it is possible toimprove characteristics while minimizing an increase in the amount ofcomputation by giving row selection conditions, similar to the firstembodiment.

Third Embodiment

In the second embodiment, the channel matrix H and the sub-channelmatrix H_(S) obtained by extracting the number of rows corresponding tothe maximum value of the number of data streams from the channel matrixH are input to the arithmetic circuit 214 to calculate the antennaweighting coefficient matrix W. A third embodiment of the presentinvention is a combination of the first embodiment and the secondembodiment of the present invention, and uses the channel matrix H andthe sub-channel matrix H_(S) to calculate the antenna weightingcoefficient matrix W, which will be described below.

FIG. 11 is a diagram illustrating the structure of an arithmetic circuit414 according to the third embodiment of the present invention.Hereinafter, the structure of the arithmetic circuit 414 according tothe third embodiment of the present invention will be described withreference to FIG. 11.

The arithmetic circuit 414 shown in FIG. 11 replaces the arithmeticcircuit 414 according to the first embodiment of the present inventionshown in FIG. 2. That is, the arithmetic circuit 414 receives thesub-channel matrix H_(S) selected by the selecting circuit 212 and thechannel matrix H estimated by the channel estimating circuit 102, andoutputs the antenna weighting coefficient matrix W for a transmissionbeam-forming.

As shown in FIG. 11, the arithmetic circuit 414 according to the thirdembodiment of the present invention includes first coefficientcalculating circuits 422 a, 422 b, and 422 c, matrix multiplyingcircuits 424 a and 424 b, and transposition circuits 426 a, 426 b, and426 c.

The first coefficient calculating circuit 422 a receives the channelmatrix H and the sub-channel matrix H_(S), and calculates the temporaryantenna weighting coefficient matrix W_(T) based on the channel matrix Hand the sub-channel matrix H_(S). The structure of the first coefficientcalculating circuit 422 a will be described below. The first coefficientcalculating circuit 422 a outputs the calculated temporary antennaweighting coefficient matrix W_(T) to the matrix multiplying circuit 424a.

The matrix multiplying circuit 424 a is for multiplying matrices. Thematrix multiplying circuit 424 a according to this embodiment multipliesthe channel matrix H estimated by the channel estimating circuit 102 bythe temporary antenna weighting coefficient matrix W_(T) calculated bythe first coefficient calculating circuit 422 a to generate a weightedchannel matrix H_(W) (H_(W)=HW_(T)). In this case, H_(W) indicates amatrix of M rows and S columns. The weighted channel matrix H_(W)generated by the matrix multiplying circuit 424 a is output to thetransposition circuit 426 a.

The transposition circuit 426 a is for transposing a matrix. Thetransposition circuit 426 a according to this embodiment receives theweighted channel matrix H_(W) generated by the matrix multiplyingcircuit 424 a, transposes the weighted channel matrix H_(W), and outputsa transposed weighted channel matrix H_(W) ^(T). The transposed weightedchannel matrix H_(W) ^(T) is input to the first coefficient calculatingcircuit 422 b.

The transposition circuit 426 b is for transposing a matrix. Thetransposition circuit 426 b according to this embodiment receives thechannel matrix H estimated by the channel estimating circuit 102,transposes the channel matrix H, and outputs a transposed channel matrixH^(T). The transposed channel matrix H^(T) is input to the firstcoefficient calculating circuit 422 b and the matrix multiplying circuit424 b.

The first coefficient calculating circuit 422 b calculates a reverseantenna weighting coefficient matrix W_(R) based on the transposedweighted channel matrix H_(W) ^(T) output from the transposition circuit426 a and the transposed channel matrix H^(T) output from thetransposition circuit 426 b. The first coefficient calculating circuit422 b outputs the calculated reverse antenna weighting coefficientmatrix W_(R) to the matrix multiplying circuit 424 b.

The matrix multiplying circuit 424 b is for multiplying matrices. Thematrix multiplying circuit 424 b according to this embodiment multipliesthe reverse antenna weighting coefficient matrix W_(R) output from thefirst coefficient calculating circuit 422 b by the transposed channelmatrix H^(T) output from the transposition circuit 426 b to generate areverse weighted channel matrix H_(WR) (H_(WR)=H^(T)W_(R)). In the case,the reverse weighted channel matrix H_(WR) includes N rows and Scolumns. The reverse weighted channel matrix H_(WR) generated by thematrix multiplying circuit 424 b is input to the transposition circuit426 c.

The transposition circuit 426 c is for transposing a matrix. Thetransposition circuit 426 c according to this embodiment transposes thereverse weighted channel matrix H_(WR) generated by the matrixmultiplying circuit 424 b to generate a transposed reverse weightedchannel matrix H_(WR) ^(T). The transposed reverse weighted channelmatrix H_(WR) ^(T) is input to the first coefficient calculating circuit422 c.

The first coefficient calculating circuit 422 c receives the channelmatrix H estimated by the channel estimating circuit 102 and thetransposed reverse weighted channel matrix H_(WR) ^(T) output from thetransposition circuit 426 c, and generates the antenna weightingcoefficient matrix W based on the channel matrix H and the transposedreverse weighted channel matrix H_(WR) ^(T). The first coefficientcalculating circuit 422 c outputs the calculated antenna weightingcoefficient matrix W to the weighting circuit 110.

The antenna weighting coefficient matrix W output from the firstcoefficient calculating circuit 422 c may be input to the matrixmultiplying circuit 424 a again. It is possible to improve communicationcharacteristics by repeatedly performing a computing process using theantenna weighting coefficient matrix W output from the first coefficientcalculating circuit 422 c.

The structure of the arithmetic circuit 414 according to the thirdembodiment of the present invention has been described above withreference to FIG. 11. Next, the structure of a first coefficientcalculating circuit according to the third embodiment of the presentinvention will be described.

FIG. 12 is a diagram illustrating the structure of the first coefficientcalculating circuit 422 a according to the third embodiment of thepresent invention. Hereinafter, the structure of the first coefficientcalculating circuit 422 a according to the fourth embodiment of thepresent invention will be described with reference to FIG. 12.

The first coefficient calculating circuit 422 a receives a sub-channelmatrix A and a channel matrix B, and calculates an antenna weightingcoefficient matrix W_(A). In the arithmetic circuit 414 shown in FIG.11, three first coefficient calculating circuits are used. Therelationship among A, B, and W_(A) in the first coefficient calculatingcircuit 422 a is (H_(S), H, W_(T)), the relationship among A, B, andW_(A) in the first coefficient calculating circuit 422 b is (H_(W) ^(T),Hr, W_(R)), and the relationship among A, B, and W_(A) in the firstcoefficient calculating circuit 422 c is (H_(WR) ^(T), H, W). In thisembodiment, only the first coefficient calculating circuit 422 a isdescribed, but the first coefficient calculating circuits 422 b and 422c have the same structure as the first coefficient calculating circuit422 a.

As shown in FIG. 12, the first coefficient calculating circuit 422 aaccording to the third embodiment of the present invention includes asecond coefficient calculating circuit 432, matrix multiplying circuits434 a and 434 b, and a SVD circuit 436. The structure of the firstcoefficient calculating circuit 422 a shown in FIG. 11 is the same asthat of the arithmetic circuit 214 according to the second embodiment ofthe present invention shown in FIG. 7.

The second coefficient calculating circuit 432 calculates a temporaryantenna weighting coefficient matrix W_(AT) based on the sub-channelmatrix A (in this embodiment, the sub-channel matrix H_(S)). Forexample, the second coefficient calculating circuit 432 can use singularvalue decomposition to calculate the temporary antenna weightingcoefficient matrix W_(AT). When singular value decomposition isperformed on the sub-channel matrix A, the following Expression 15 isobtained.

A=UDV^(H)  (Expression 15)

When the temporary antenna weighting coefficient matrix W_(AT) iscalculated by singular value decomposition, the second coefficientcalculating circuit 432 uses the matrix (right singular matrix) V ofExpression 15 as the temporary antenna weighting coefficient matrixW_(AT). The second coefficient calculating circuit 432 outputs thecalculated temporary antenna weighting coefficient matrix W_(AT) to thematrix multiplying circuits 434 a and 434 b.

The matrix multiplying circuit 434 a is for multiplying matrices. Thematrix multiplying circuit 434 a according to this embodiment multipliesthe channel matrix B (in this embodiment, the channel matrix H) by thetemporary antenna weighting coefficient matrix W_(AT) calculated by thesecond coefficient calculating circuit 432 to generate a weightedchannel matrix H_(AW) (H_(AW)=BW_(AT)). In this case, H_(AW) indicates amatrix of M rows and S columns. The weighted channel matrix H_(AW)generated by the matrix multiplying circuit 434 a is output to the SVDcircuit 436.

The SVD circuit 436 is for performing singular value decomposition. Inthis embodiment, the SVD circuit 436 receives the weighted channelmatrix H_(AW) generated by the matrix multiplying circuit 434 a andperforms the singular value decomposition on the weighted channel matrixH_(AW). When the singular value decomposition is performed on theweighted channel matrix H_(AW), the following Expression 16 is obtained.

H_(AW)=UDV^(H)  (Expression 16)

The matrix (right singular matrix) V obtained by the Expression 16 isoutput to the matrix multiplying circuit 434 b.

The matrix multiplying circuit 434 b is for multiplying matrices. Thematrix multiplying circuit 434 b according to this embodiment multipliesthe temporary antenna weighting coefficient matrix W_(AT) calculated bythe second coefficient calculating circuit 432 by the matrix Vcalculated by the SVD circuit 436 to generate the antenna weightingcoefficient matrix W_(A) (W_(A)=W_(AT)V). In this embodiment, theantenna weighting coefficient matrix W_(A) is the temporary antennaweighting coefficient matrix W_(T). The rows that are not used in thecalculation of the temporary antenna weighting coefficient matrix W_(AT)(that is, the rows of the channel matrix B that are not included in thesub-channel matrix A) are used to calculate the antenna weightingcoefficient matrix W_(A). Therefore, it is possible to improvecharacteristics.

The antenna weighting coefficient matrix W_(A) generated by the matrixmultiplying circuit 434 b is the reverse antenna weighting coefficientmatrix W_(R) in the first coefficient calculating circuit 422 b, and isthe antenna weighting coefficient matrix W in the first coefficientcalculating circuit 422 c.

The structure of the first coefficient calculating circuit 422 aaccording to the third embodiment of the present invention has beendescribed with reference to FIG. 12. Next, a method of calculating theantenna weighting coefficient matrix W according to the third embodimentof the present invention will be described.

FIG. 13 is a flowchart illustrating the method of calculating theantenna weighting coefficient matrix W according to the third embodimentof the present invention. Hereinafter, the method of calculating theantenna weighting coefficient matrix W according to the third embodimentof the present invention will be described with reference to FIG. 13.

When the channel matrix H of N rows and M columns is input to theweighting coefficient matrix calculating circuit 104, the selectingcircuit 112 appropriately selects S rows from the channel matrix H andgenerates the sub-channel matrix H_(S) (Step S402). When the selectingcircuit 112 generates the sub-channel matrix H_(S), the firstcoefficient calculating circuit 422 a calculates the temporary antennaweighting coefficient matrix W_(T) based on the generated sub-channelmatrix H_(S) and the channel matrix H (Step S404). As described above,for example, the first coefficient calculating circuit 422 a can usesingular value decomposition to calculate the temporary antennaweighting coefficient matrix W_(T).

When the temporary antenna weighting coefficient matrix W_(T) iscompletely generated in Step S404, the matrix multiplying circuit 424 amultiplies the channel matrix H by the temporary antenna weightingcoefficient matrix W_(T) to generate the weighted channel matrix H_(W)(Step S406).

When the weighted channel matrix H_(W) is generated in Step S406, thetransposition circuit 426 a transposes the weighted channel matrixH_(W). Then, the first coefficient calculating circuit 422 b receivesthe transposed weighted channel matrix H_(W) ^(T) and the transposedmatrix H^(T) of the channel matrix H, and calculates the reverse antennaweighting coefficient matrix W_(R) using the transposed weighted channelmatrix H_(W) ^(T) and the transposed matrix H^(T) of the channel matrixH (Step S408).

When the reverse antenna weighting coefficient matrix W_(R) is generatedin Step S408, the matrix multiplying circuit 424 b multiplies thetransposed channel matrix H^(T) transposed from the channel matrix H bythe transposition circuit 426 b by the reverse antenna weightingcoefficient matrix W_(R). A reverse weighted channel matrix H_(WR) isobtained by the multiplication of the transposed channel matrix H^(T)and the reverse antenna weighting coefficient matrix W_(R) (Step S410).

When the reverse weighted channel matrix H_(WR) is obtained in StepS410, the transposition circuit 426 c transposes the reverse weightedchannel matrix H_(WR) to obtain a transposed reverse weighted channelmatrix H_(WR) ^(T). Then, the first coefficient calculating circuit 422c receives the transposed reverse weighted channel matrix H_(WR) ^(T)and the channel matrix H and calculates the antenna weightingcoefficient matrix W (Step 412).

In this way, finally, the matrix is transposed to obtain the antennaweighting coefficient matrix W. Therefore, it is possible to obtain thesame effects as those when the arithmetic circuit 414 performs a two-waybeam-forming process.

From the relationship of S<min(M, N), since the rank of the matrixsubjected to singular value decomposition is less than that in eigenmodetransmission, the amount of calculation is reduced by a valuecorresponding to the reduction in the rank of the matrix. In addition,the amount of calculation is reduced by a value corresponding to areduction in the number of elements of the matrix generated by thesingular value decomposition. In addition, in order to improvecommunication characteristics, Step S406 to Step S412 may be repeatedlyperformed using the antenna weighting coefficient matrix W output fromthe first coefficient calculating circuit 422 c. As described above, thetotal number of processes is increased, but the amount of calculation ofthe complicated singular value decomposition process is reduced.Therefore, it is possible to prevent an increase in the total amount ofcomputation.

The first coefficient calculating circuits 422 a, 422 b, and 422 c maybe integrated with each other, the matrix multiplying circuits 424 a,424 b, 424 c, 434 a, and 434 b may be integrated with each other, andthe transposition circuits 426 a, 426 b, and 426 c may be integratedwith each other according to conditions. In addition, the secondcoefficient calculating circuit 432 and the SVD circuit 436 may beintegrated with each other.

FIGS. 14A to 14F are line graphs illustrating the comparison betweeneigenmode transmission and transmission using the antenna weightingcoefficient matrix W calculated by the method of calculating the antennaweighting coefficient matrix W according to the third embodiment of thepresent invention. FIGS. 14A to 14F plot the maximum value and theminimum value of the ratios of communication capacity C₁ when S datastreams are transmitted by the antenna weighting coefficient matrix W tocommunication capacity C₀ when S data streams are transmitted byeigenmode transmission (C₁/C₀) for 10000 samples. In the line graphs,the horizontal axis indicates the number of times Steps S406 to S412 arerepeated. In addition, the number of repetitions is zero when thetemporary antenna weighting matrix W_(T) calculated by the firstcoefficient calculating circuit 422 a is used instead of the antennaweighting coefficient matrix W.

In FIGS. 14A to 14F, when the number of transmitting antennas is M, thenumber of receiving antennas is N, and the number of rows selected fromthe channel matrix is S, the ratio of communication capacities isrepresented by six combinations of M, N, and S.

Here, the communication capacity C is calculated by the above-mentionedExpression 9. In addition, it is assumed that each element of thechannel matrix H has a complex normal distribution with a mean of 0 anda variance of 1.

FIG. 14A is a line graph illustrating characteristics when (M×N×S) is(3×3×2), FIG. 14B is a line graph illustrating characteristics when(M×N×S) is (4×4×2), and FIG. 14C is a line graph illustratingcharacteristics when (M×N×S) is (4×4×3). In addition, FIG. 14D is a linegraph illustrating characteristics when (M×N×S) is (5×5×2), FIG. 14E isa line graph illustrating characteristics when (M×N×S) is (5×5×3), andFIG. 14F is a line graph illustrating characteristics when (M×N×S) is(5×5×4).

As can be seen from six line graphs shown in FIGS. 14A to 14F, similarto the line graphs according to the first embodiment of the presentinvention, in all the cases, the ratio of the communication capacitiesconverges on 1. That is, the communication capacity when the antennaweighting coefficient matrix W is used converges on the communicationcapacity during the eigenmode transmission.

The method of calculating the antenna weighting coefficient matrix Waccording to the third embodiment of the present invention has beendescribed above. Next, modifications of the third embodiment of thepresent invention will be described.

FIG. 15 is a diagram illustrating the structure of an arithmetic circuit414′ according to a modification of the third embodiment of the presentinvention. Hereinafter, the structure of the arithmetic circuit 414′according to the modification of the third embodiment of the presentinvention will be described with reference to FIG. 15.

The arithmetic circuit 414′ shown in FIG. 15 includes second coefficientcalculating circuits 432 a and 432 b, instead of the first coefficientcalculating circuits 422 b and 422 c of the arithmetic circuit 414 shownin FIG. 11. Even when the arithmetic circuit 414′ has theabove-mentioned structure, it is possible to obtain the samecommunication characteristics as those obtained by the arithmeticcircuit 414 shown in FIG. 11.

Combinations of the first coefficient calculating circuit and the secondcoefficient calculating circuit are not limited to the above. Forexample, the second coefficient calculating circuit may be providedinstead of the first coefficient calculating circuit 422 b, and thesecond coefficient calculating circuit may be provided instead of thefirst coefficient calculating circuit 422 c.

The structure of the arithmetic circuit 414′ according to themodification of the third embodiment of the present invention has beendescribed above. Next, the operation of the selecting circuit 112selecting S rows from the channel matrix H by the patterns 1 to 5described in the first embodiment of the present invention in order toimprove the convergence speed of the communication capacity which willbe described.

FIGS. 16A to 16F are line graphs illustrating the convergence ofcommunication capacity when the antenna weighting coefficient matrix Wis calculated using the sub-channel matrix H_(S) selected by thepatterns 1 to 5. In FIGS. 16A to 16F show the antenna weightingcoefficient matrix W calculated using the sub-channel matrix H_(S) thatis selected by the patterns 1 to 5 when (M×N×S) is (3×3×2).

FIG. 16A is the same as FIG. 14A, FIG. 16B is a line graph illustratingcharacteristics when the sub-channel matrix H_(S) selected by thepattern 1 is used, and FIG. 16C is a line graph illustratingcharacteristics when the sub-channel matrix H_(S) selected by thepattern 2 is used. FIG. 16D is a line graph illustrating characteristicswhen the sub-channel matrix H_(S) selected by the pattern 3 is used,FIG. 16E is a line graph illustrating characteristics when thesub-channel matrix H_(S) selected by the pattern 4 is used, and FIG. 16Fis a line graph illustrating characteristics when the sub-channel matrixH_(S) selected by the pattern 5 is used.

As can be seen from the graphs shown in FIGS. 16A to 16F, similar to theline graphs shown in the first embodiment of the present invention, inall the patterns 1 to 5, convergence characteristics are improved, ascompared to when S rows are appropriately selected from N rows of thechannel matrix H of N rows and M columns to generate the sub-channelmatrix H_(S). The improvement in the convergence characteristics makesit possible to shorten a calculation time.

In this embodiment, (M×N×S) is (3×3×2), but other combinations of M, N,and S may be used. In this case, the convergence characteristics arealso improved, as compared to when S rows are appropriately selectedfrom N rows of the channel matrix H of N rows and M columns to generatethe sub-channel matrix H_(S).

As described above, according to the third embodiment of the presentinvention, when the maximum value of the number of data streams to besubjected to a transmission beam-forming process is smaller than thenumber of antennas, the antenna weighting coefficient matrix W iscalculated based on the channel matrix H and the sub-channel matrixH_(S) obtained by extracting the number of rows corresponding to themaximum value of the number of data streams from the channel matrix H.

As such, since the antenna weighting coefficient matrix W is calculatedbased on the channel matrix H and the sub-channel matrix H_(S), the rankof the matrix used for calculation is reduced, and it is possible toreduce the amount of calculation of SVD, which is a representativeexample of the matrix computation, while maintaining characteristics ata certain level. In addition, it is possible to prevent deterioration ofcharacteristics and reduce the overall size of a weighting coefficientmatrix calculating circuit. When this wireless communication system isactually mounted to a communication apparatus, the amount of computationdepends on the size of a circuit and a computation time. Therefore, itis possible to reduce the size of a circuit and shorten the computationtime.

When the sub-channel matrix H_(S) is generated, it is possible toimprove convergence characteristics while minimizing an increase in theamount of computation by giving row selection conditions, similar to thefirst and second embodiments of the present invention.

In addition, a computer program for estimating a matrix by theabove-mentioned method or performing an operation on a matrix may bestored in the transmitter 100 or the receiver 200, and a computingdevice, such as a CPU (central processing unit) may read the storedcomputer programs and execute it to implement the method of calculatingthe antenna weighting coefficient matrix W according to any one of thefirst to third embodiments of the present invention.

The present application contains subject matter related to thatdisclosed in Japanese Priority Patent Application JP 2008-120598 filedin the Japan Patent Office on May 2, 2008, the entire content of whichis hereby incorporated by reference.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

1. A wireless communication apparatus comprising: a matrix estimatingunit that estimates a channel matrix of N rows and M columns (N and Mare natural numbers); a selecting unit that selects S rows (S is anatural number, and S<MIN(M, N)) from the channel matrix estimated bythe matrix estimating unit and generates a sub-channel matrix of S rowsand M columns; and an arithmetic unit that calculates an antennaweighting coefficient matrix based on the channel matrix estimated bythe matrix estimating unit and the sub-channel matrix generated by theselecting unit, wherein the arithmetic unit includes: a weighted channelmatrix generating unit that multiplies the channel matrix by a temporaryantenna weighting coefficient matrix calculated based on the sub-channelmatrix to generate a weighted channel matrix; a reverse weighted channelmatrix generating unit that transposes the channel matrix and theweighted channel matrix, and multiplies the transposed channel matrix bya reverse antenna weighting coefficient matrix calculated based on thetransposed weighted channel matrix to generate a reverse weightedchannel matrix; and an antenna weighting coefficient matrix calculatingunit that calculates the antenna weighting coefficient matrix based on atransposed reverse weighted channel matrix.
 2. The wirelesscommunication apparatus according to claim 1, wherein the weightedchannel matrix generating unit repeatedly generates the weighted channelmatrix using the antenna weighting coefficient matrix calculated by theantenna weighting coefficient matrix calculating unit instead of thetemporary antenna weighting coefficient matrix.
 3. The wirelesscommunication apparatus according to claim 1, wherein the arithmeticunit includes: a first coefficient calculating unit that calculates thetemporary antenna weighting coefficient matrix based on the sub-channelmatrix; a first matrix multiplying unit that multiplies the channelmatrix by the temporary antenna weighting coefficient matrix to generatethe weighted channel matrix; a first transposition unit that transposesthe channel matrix and the weighted channel matrix; a second coefficientcalculating unit that calculates the reverse antenna weightingcoefficient matrix based on the transposed weighted channel matrix; asecond matrix multiplying unit that multiplies the transposed channelmatrix by the reverse antenna weighting coefficient matrix to generatethe reverse weighted channel matrix; a second transposition unit thattransposes the reverse weighted channel matrix; and a third coefficientcalculating unit that calculates the antenna weighting coefficientmatrix based on the transposed reverse weighted channel matrix.
 4. Thewireless communication apparatus according to claim 3, wherein the firstcoefficient calculating unit calculates the temporary antenna weightingcoefficient matrix based on the sub-channel matrix and the channelmatrix, the second coefficient calculating unit calculates the reverseantenna weighting coefficient matrix based on the transposed weightedchannel matrix and the transposed channel matrix, and the thirdcoefficient calculating unit calculates the antenna weightingcoefficient matrix based on the transposed reverse weighted channelmatrix and the channel matrix.
 5. The wireless communication apparatusaccording to claim 3, wherein at least one of the first to thirdcoefficient calculating units includes: a fourth coefficient calculatingunit that calculates the temporary antenna weighting coefficient matrixbased on the sub-channel matrix; a third matrix multiplying unit thatmultiplies the channel matrix by the temporary antenna weightingcoefficient matrix to generate the weighted channel matrix; a singularvalue decomposing unit that performs singular value decomposition on theweighted channel matrix; and a fourth matrix multiplying unit thatmultiplies a matrix obtained by the singular value decomposition by thetemporary antenna weighting coefficient matrix to generate the antennaweighting coefficient matrix.
 6. The wireless communication apparatusaccording to claim 1, wherein the selecting unit selects the S rows thatallow the sum of the squares of elements of a matrix to be the maximum.7. The wireless communication apparatus according to claim 1, whereinthe selecting unit selects the S rows that allow an eigenvalue of acovariance matrix of a matrix or the maximum value of a singular valueof the matrix to be the maximum.
 8. The wireless communication apparatusaccording to claim 1, wherein the selecting unit selects the S rows thatallow an eigenvalue of a covariance matrix of a matrix or the minimumvalue of a singular value of the matrix to be the maximum.
 9. Thewireless communication apparatus according to claim 1, wherein theselecting unit selects the S rows that allow a determinant of acovariance matrix of a matrix to be the maximum.
 10. The wirelesscommunication apparatus according to claim 1, wherein the selecting unitselects the S rows that allow a value obtained by dividing a determinantof a covariance matrix of a matrix by the sum of the squares of elementsof the matrix to be the maximum.
 11. A wireless communication methodcomprising the steps of: estimating a channel matrix of N rows and Mcolumns (N and M are natural numbers); selecting S rows (S is a naturalnumber, and S<MIN(M, N)) from the estimated channel matrix andgenerating a sub-channel matrix of S rows and M columns; and calculatingan antenna weighting coefficient matrix based on the estimated channelmatrix and the generated sub-channel matrix, wherein the calculatingstep includes the sub-steps of: multiplying the channel matrix by atemporary antenna weighting coefficient matrix calculated based on thesub-channel matrix to generate a weighted channel matrix; transposingthe channel matrix and the weighted channel matrix, and multiplying thetransposed channel matrix by a reverse antenna weighting coefficientmatrix calculated based on the transposed weighted channel matrix togenerate a reverse weighted channel matrix; and calculating the antennaweighting coefficient matrix based on a transposed reverse weightedchannel matrix.
 12. A computer program for allowing a computer toexecute the steps of: estimating a channel matrix of N rows and Mcolumns (N and M are natural numbers); selecting S rows (S is a naturalnumber, and S<MIN(M, N)) from the estimated channel matrix andgenerating a sub-channel matrix of S rows and M columns; and calculatingan antenna weighting coefficient matrix based on the estimated channelmatrix and the generated sub-channel matrix, wherein the calculatingstep includes the sub-steps of: multiplying the channel matrix by atemporary antenna weighting coefficient matrix calculated based on thesub-channel matrix to generate a weighted channel matrix; transposingthe channel matrix and the weighted channel matrix, and multiplying thetransposed channel matrix by a reverse antenna weighting coefficientmatrix calculated based on the transposed weighted channel matrix togenerate a reverse weighted channel matrix; and calculating the antennaweighting coefficient matrix based on a transposed reverse weightedchannel matrix.
 13. A wireless communication system comprising: a firstwireless communication apparatus; and a second wireless communicationapparatus, wherein the first wireless communication apparatus includes atransmitting unit that transmits reference signals to the secondwireless communication apparatus through a plurality of antennas, thesecond wireless communication apparatus includes: a matrix estimatingunit that estimates a channel matrix of N rows and M columns (N and Mare natural numbers) based on the reference signals received through aplurality of antennas; a selecting unit that selects S rows (S is anatural number, and S<MIN(M, N)) from the channel matrix estimated bythe matrix estimating unit and generates a sub-channel matrix of S rowsand M columns; and an arithmetic unit that calculates an antennaweighting coefficient matrix based on the channel matrix estimated bythe matrix estimating unit and the sub-channel matrix generated by theselecting unit, and the arithmetic unit includes: a weighted channelmatrix generating unit that multiplies the channel matrix by a temporaryantenna weighting coefficient matrix calculated based on the sub-channelmatrix to generate a weighted channel matrix; a reverse weighted channelmatrix generating unit that transposes the channel matrix and theweighted channel matrix, and multiplies the transposed channel matrix bya reverse antenna weighting coefficient matrix calculated based on thetransposed weighted channel matrix to generate a reverse weightedchannel matrix; and an antenna weighting coefficient matrix calculatingunit that calculates the antenna weighting coefficient matrix based on atransposed reverse weighted channel matrix.